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                        第5节：Numpy索引和切片作业
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                            <b>2.6.</b>
                        
                        第6节：numpy数组操作
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                        第7节：深拷贝和浅拷贝
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                        第8节：文件操作
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                        第12节：axis理解.md
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                        第4节：pandas函数应用
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                        第6节：pandas统计计算和描述
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                        第8节：数据清洗
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                        第9节：聚合和分组
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                        第10节：时间序列
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                        第四章：Matploblib库
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                        第1节：常用图
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                        第2节：基本使用
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                        第3节：条形图
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                        第4节：直方图
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                        第5节：散点图
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                        第6节：饼图
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                        第7节：箱线图
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                        第8节：雷达图
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                            <b>4.9.</b>
                        
                        第9节：matplotlib绘图分析
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                            <b>4.10.</b>
                        
                        第10节：多图布局
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                            <b>4.11.</b>
                        
                        第11节：matplotlib配置
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                            <b>4.12.</b>
                        
                        第12节：matplotlib作业
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                            <b>5.</b>
                        
                        第五章：Seaborn库
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                            <b>5.1.</b>
                        
                        第1节：关系绘图
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                        第2节：分类绘图
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                            <b>5.3.</b>
                        
                        第3节：分布绘图
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                            <b>5.4.</b>
                        
                        第4节：线性关系绘图
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                            <b>5.5.</b>
                        
                        第5节：FacetGrid结构图
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                            <b>5.6.</b>
                        
                        第6节：样式设置
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                            <b>5.7.</b>
                        
                        第7节：调色盘设置
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                            <b>5.8.</b>
                        
                        第8节：seaborn作业
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                            <b>6.</b>
                        
                        第六章：统计分析强化
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                            <b>6.1.</b>
                        
                        第1节：常用专业数学术语
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                            <b>6.2.</b>
                        
                        第2节：平均数
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                            <b>6.3.</b>
                        
                        第3节：方差和标准差
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                            <b>6.4.</b>
                        
                        第4节：正态分布
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                            <b>6.5.</b>
                        
                        第5节：对比分析
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                            <b>6.6.</b>
                        
                        第6节：分布分析
                    </a>
            
            
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                            <b>6.7.</b>
                        
                        第7节：交叉分析
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                            <b>6.8.</b>
                        
                        第8节：统计分析
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                            <b>6.9.</b>
                        
                        第9节：帕累托分析
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                            <b>6.10.</b>
                        
                        第10节：矩阵关联分析
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                            <b>6.11.</b>
                        
                        第11节：综合性分析
                    </a>
            
            
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            </ul>
            
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                            <b>7.</b>
                        
                        第七章：数据分析实战
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                            <b>7.1.</b>
                        
                        第1节：App数据分析实战
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                            <b>7.2.</b>
                        
                        第2节：心脏病患者数据分析
                    </a>
            
            
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        <li class="chapter " data-level="7.3" data-path="chapter6/03StackoverFlow数据分析.html">
            
                
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                            <b>7.3.</b>
                        
                        第3节：StackoverFlow数据分析
                    </a>
            
            
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        <li class="chapter " data-level="7.4" data-path="chapter6/04二手房数据分析.html">
            
                
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                            <b>7.4.</b>
                        
                        第4节：二手房数据分析
                    </a>
            
            
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        <li class="chapter " data-level="7.5" data-path="chapter6/05吃鸡数据分析.html">
            
                
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                            <b>7.5.</b>
                        
                        第5节：吃鸡数据分析
                    </a>
            
            
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        <li class="chapter " data-level="7.6" data-path="chapter6/06黑色星期五数据分析.html">
            
                
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                            <b>7.6.</b>
                        
                        第6节：黑色星期五数据分析
                    </a>
            
            
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            </ul>
            
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                            <b>8.</b>
                        
                        第八章：补充
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                            <b>8.1.</b>
                        
                        第1节：用Excel做数据分析
                    </a>
            
            
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                            <b>8.2.</b>
                        
                        第2节：echarts和pyecharts库
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                            <b>8.3.</b>
                        
                        第3节：bokeh库
                    </a>
            
            
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                        <h1 id="&#x901A;&#x7528;&#x51FD;&#x6570;">&#x901A;&#x7528;&#x51FD;&#x6570;</h1>
<h2 id="&#x4E00;&#x5143;&#x51FD;&#x6570;&#xFF1A;">&#x4E00;&#x5143;&#x51FD;&#x6570;&#xFF1A;</h2>
<table>
<thead>
<tr>
<th>&#x51FD;&#x6570;</th>
<th>&#x63CF;&#x8FF0;</th>
</tr>
</thead>
<tbody>
<tr>
<td>np.abs</td>
<td>&#x7EDD;&#x5BF9;&#x503C;</td>
</tr>
<tr>
<td>np.sqrt</td>
<td>&#x5F00;&#x6839;</td>
</tr>
<tr>
<td>np.square</td>
<td>&#x5E73;&#x65B9;</td>
</tr>
<tr>
<td>np.exp</td>
<td>&#x8BA1;&#x7B97;&#x6307;&#x6570;(e^x)</td>
</tr>
<tr>
<td>np.log&#xFF0C;np.log10&#xFF0C;np.log2&#xFF0C;np.log1p</td>
<td>&#x6C42;&#x4EE5;e&#x4E3A;&#x5E95;&#xFF0C;&#x4EE5;10&#x4E3A;&#x4F4E;&#xFF0C;&#x4EE5;2&#x4E3A;&#x4F4E;&#xFF0C;&#x4EE5;(1+x)&#x4E3A;&#x5E95;&#x7684;&#x5BF9;&#x6570;</td>
</tr>
<tr>
<td>np.sign</td>
<td>&#x5C06;&#x6570;&#x7EC4;&#x4E2D;&#x7684;&#x503C;&#x6807;&#x7B7E;&#x5316;&#xFF0C;&#x5927;&#x4E8E;0&#x7684;&#x53D8;&#x6210;1&#xFF0C;&#x7B49;&#x4E8E;0&#x7684;&#x53D8;&#x6210;0&#xFF0C;&#x5C0F;&#x4E8E;0&#x7684;&#x53D8;&#x6210;-1</td>
</tr>
<tr>
<td>np.ceil</td>
<td>&#x671D;&#x7740;&#x65E0;&#x7A77;&#x5927;&#x7684;&#x65B9;&#x5411;&#x53D6;&#x6574;&#xFF0C;&#x6BD4;&#x5982;5.1&#x4F1A;&#x53D8;&#x6210;6&#xFF0C;-6.3&#x4F1A;&#x53D8;&#x6210;-6</td>
</tr>
<tr>
<td>np.floor</td>
<td>&#x671D;&#x7740;&#x8D1F;&#x65E0;&#x7A77;&#x5927;&#x65B9;&#x5411;&#x53D6;&#x8BC1;&#xFF0C;&#x6BD4;&#x5982;5.1&#x4F1A;&#x53D8;&#x6210;5&#xFF0C;-6.3&#x4F1A;&#x53D8;&#x6210;-7</td>
</tr>
<tr>
<td>np.rint&#xFF0C;np.round</td>
<td>&#x8FD4;&#x56DE;&#x56DB;&#x820D;&#x4E94;&#x5165;&#x540E;&#x7684;&#x503C;</td>
</tr>
<tr>
<td>np.modf</td>
<td>&#x5C06;&#x6574;&#x6570;&#x548C;&#x5C0F;&#x6570;&#x5206;&#x9694;&#x5F00;&#x6765;&#x5F62;&#x6210;&#x4E24;&#x4E2A;&#x6570;&#x7EC4;</td>
</tr>
<tr>
<td>np.isnan</td>
<td>&#x5224;&#x65AD;&#x662F;&#x5426;&#x662F;nan</td>
</tr>
<tr>
<td>np.isinf</td>
<td>&#x5224;&#x65AD;&#x662F;&#x5426;&#x662F;inf</td>
</tr>
<tr>
<td>np.cos&#xFF0C;np.cosh&#xFF0C;np.sin&#xFF0C;np.sinh&#xFF0C;np.tan&#xFF0C;np.tanh</td>
<td>&#x4E09;&#x89D2;&#x51FD;&#x6570;</td>
</tr>
<tr>
<td>np.arccos&#xFF0C;np.arcsin&#xFF0C;np.arctan</td>
<td>&#x53CD;&#x4E09;&#x89D2;&#x51FD;&#x6570;</td>
</tr>
</tbody>
</table>
<h2 id="&#x4E8C;&#x5143;&#x51FD;&#x6570;&#xFF1A;">&#x4E8C;&#x5143;&#x51FD;&#x6570;&#xFF1A;</h2>
<table>
<thead>
<tr>
<th>&#x51FD;&#x6570;</th>
<th>&#x63CF;&#x8FF0;</th>
</tr>
</thead>
<tbody>
<tr>
<td>np.add</td>
<td>&#x52A0;&#x6CD5;&#x8FD0;&#x7B97;&#xFF08;&#x5373;1+1=2&#xFF09;&#xFF0C;&#x76F8;&#x5F53;&#x4E8E;+</td>
</tr>
<tr>
<td>np.subtract</td>
<td>&#x51CF;&#x6CD5;&#x8FD0;&#x7B97;&#xFF08;&#x5373;3-2=1&#xFF09;&#xFF0C;&#x76F8;&#x5F53;&#x4E8E;-</td>
</tr>
<tr>
<td>np.negative</td>
<td>&#x8D1F;&#x6570;&#x8FD0;&#x7B97;&#xFF08;&#x5373;-2&#xFF09;&#xFF0C;&#x76F8;&#x5F53;&#x4E8E;&#x52A0;&#x4E2A;&#x8D1F;&#x53F7;</td>
</tr>
<tr>
<td>np.multiply</td>
<td>&#x4E58;&#x6CD5;&#x8FD0;&#x7B97;&#xFF08;&#x5373;2*3=6&#xFF09;&#xFF0C;&#x76F8;&#x5F53;&#x4E8E;*</td>
</tr>
<tr>
<td>np.divide</td>
<td>&#x9664;&#x6CD5;&#x8FD0;&#x7B97;&#xFF08;&#x5373;3/2=1.5&#xFF09;&#xFF0C;&#x76F8;&#x5F53;&#x4E8E;/</td>
</tr>
<tr>
<td>np.floor_divide</td>
<td>&#x53D6;&#x6574;&#x8FD0;&#x7B97;&#xFF0C;&#x76F8;&#x5F53;&#x4E8E;//</td>
</tr>
<tr>
<td>np.mod</td>
<td>&#x53D6;&#x4F59;&#x8FD0;&#x7B97;&#xFF0C;&#x76F8;&#x5F53;&#x4E8E;%</td>
</tr>
<tr>
<td>greater,greater_equal,less,less_equal,equal,not_equal</td>
<td>&gt;,&gt;=,&lt;,&lt;=,=,!=&#x7684;&#x51FD;&#x6570;&#x8868;&#x8FBE;&#x5F0F;</td>
</tr>
<tr>
<td>logical_and</td>
<td>&amp;&#x7684;&#x51FD;&#x6570;&#x8868;&#x8FBE;&#x5F0F;</td>
</tr>
<tr>
<td>logical_or</td>
<td>\</td>
<td>&#x7684;&#x51FD;&#x6570;&#x8868;&#x8FBE;&#x5F0F;</td>
</tr>
</tbody>
</table>
<h3 id="&#x805A;&#x5408;&#x51FD;&#x6570;&#xFF1A;">&#x805A;&#x5408;&#x51FD;&#x6570;&#xFF1A;</h3>
<table>
<thead>
<tr>
<th>&#x51FD;&#x6570;&#x540D;&#x79F0;</th>
<th>NAN&#x5B89;&#x5168;&#x7248;&#x672C;</th>
<th>&#x63CF;&#x8FF0;</th>
</tr>
</thead>
<tbody>
<tr>
<td>np.sum</td>
<td>np.nansum</td>
<td>&#x8BA1;&#x7B97;&#x5143;&#x7D20;&#x7684;&#x548C;</td>
</tr>
<tr>
<td>np.prod</td>
<td>np.nanprod</td>
<td>&#x8BA1;&#x7B97;&#x5143;&#x7D20;&#x7684;&#x79EF;</td>
</tr>
<tr>
<td>np.mean</td>
<td>np.nanmean</td>
<td>&#x8BA1;&#x7B97;&#x5143;&#x7D20;&#x7684;&#x5E73;&#x5747;&#x503C;</td>
</tr>
<tr>
<td>np.std</td>
<td>np.nanstd</td>
<td>&#x8BA1;&#x7B97;&#x5143;&#x7D20;&#x7684;&#x6807;&#x51C6;&#x5DEE;</td>
</tr>
<tr>
<td>np.var</td>
<td>np.nanvar</td>
<td>&#x8BA1;&#x7B97;&#x5143;&#x7D20;&#x7684;&#x65B9;&#x5DEE;</td>
</tr>
<tr>
<td>np.min</td>
<td>np.nanmin</td>
<td>&#x8BA1;&#x7B97;&#x5143;&#x7D20;&#x7684;&#x6700;&#x5C0F;&#x503C;</td>
</tr>
<tr>
<td>np.max</td>
<td>np.nanmax</td>
<td>&#x8BA1;&#x7B97;&#x5143;&#x7D20;&#x7684;&#x6700;&#x5927;&#x503C;</td>
</tr>
<tr>
<td>np.argmin</td>
<td>np.nanargmin</td>
<td>&#x627E;&#x51FA;&#x6700;&#x5C0F;&#x503C;&#x7684;&#x7D22;&#x5F15;</td>
</tr>
<tr>
<td>np.argmax</td>
<td>np.nanargmax</td>
<td>&#x627E;&#x51FA;&#x6700;&#x5927;&#x503C;&#x7684;&#x7D22;&#x5F15;</td>
</tr>
<tr>
<td>np.median</td>
<td>np.nanmedian</td>
<td>&#x8BA1;&#x7B97;&#x5143;&#x7D20;&#x7684;&#x4E2D;&#x4F4D;&#x6570;</td>
</tr>
</tbody>
</table>
<p>&#x4F7F;&#x7528;<code>np.sum</code>&#x6216;&#x8005;&#x662F;<code>a.sum</code>&#x5373;&#x53EF;&#x5B9E;&#x73B0;&#x3002;&#x5E76;&#x4E14;&#x5728;&#x4F7F;&#x7528;&#x7684;&#x65F6;&#x5019;&#xFF0C;&#x53EF;&#x4EE5;&#x6307;&#x5B9A;&#x5177;&#x4F53;&#x54EA;&#x4E2A;&#x8F74;&#x3002;&#x540C;&#x6837;<code>Python</code>&#x4E2D;&#x4E5F;&#x5185;&#x7F6E;&#x4E86;<code>sum</code>&#x51FD;&#x6570;&#xFF0C;&#x4F46;&#x662F;Python&#x5185;&#x7F6E;&#x7684;<code>sum</code>&#x51FD;&#x6570;&#x6267;&#x884C;&#x6548;&#x7387;&#x6CA1;&#x6709;<code>np.sum</code>&#x90A3;&#x4E48;&#x9AD8;&#xFF0C;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;&#x4EE5;&#x4E0B;&#x4EE3;&#x7801;&#x6D4B;&#x8BD5;&#x4E86;&#x89E3;&#x5230;&#xFF1A;</p>
<pre><code class="lang-python">a = np.random.rand(<span class="hljs-number">1000000</span>)
%timeit sum(a) <span class="hljs-comment">#&#x4F7F;&#x7528;Python&#x5185;&#x7F6E;&#x7684;sum&#x51FD;&#x6570;&#x6C42;&#x603B;&#x548C;&#xFF0C;&#x770B;&#x4E0B;&#x6240;&#x82B1;&#x8D39;&#x7684;&#x65F6;&#x95F4;</span>
%timeit np.sum(a) <span class="hljs-comment">#&#x4F7F;&#x7528;Numpy&#x7684;sum&#x51FD;&#x6570;&#x6C42;&#x548C;&#xFF0C;&#x770B;&#x4E0B;&#x6240;&#x82B1;&#x8D39;&#x7684;&#x65F6;&#x95F4;</span>
</code></pre>
<h3 id="&#x5E03;&#x5C14;&#x6570;&#x7EC4;&#x7684;&#x51FD;&#x6570;&#xFF1A;">&#x5E03;&#x5C14;&#x6570;&#x7EC4;&#x7684;&#x51FD;&#x6570;&#xFF1A;</h3>
<table>
<thead>
<tr>
<th style="text-align:left">&#x51FD;&#x6570;&#x540D;&#x79F0;</th>
<th style="text-align:left">&#x63CF;&#x8FF0;</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left">np.any</td>
<td style="text-align:left">&#x9A8C;&#x8BC1;&#x4EFB;&#x4F55;&#x4E00;&#x4E2A;&#x5143;&#x7D20;&#x662F;&#x5426;&#x4E3A;&#x771F;</td>
</tr>
<tr>
<td style="text-align:left">np.all</td>
<td style="text-align:left">&#x9A8C;&#x8BC1;&#x6240;&#x6709;&#x5143;&#x7D20;&#x662F;&#x5426;&#x4E3A;&#x771F;</td>
</tr>
</tbody>
</table>
<p>&#x6BD4;&#x5982;&#x60F3;&#x770B;&#x4E0B;&#x6570;&#x7EC4;&#x4E2D;&#x662F;&#x4E0D;&#x662F;&#x6240;&#x6709;&#x5143;&#x7D20;&#x90FD;&#x4E3A;0&#xFF0C;&#x90A3;&#x4E48;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;&#x4EE5;&#x4E0B;&#x4EE3;&#x7801;&#x6765;&#x5B9E;&#x73B0;&#xFF1A;</p>
<pre><code class="lang-python">np.all(a==<span class="hljs-number">0</span>) 
<span class="hljs-comment"># &#x6216;&#x8005;&#x662F;</span>
(a==<span class="hljs-number">0</span>).all()
</code></pre>
<p>&#x6BD4;&#x5982;&#x6211;&#x4EEC;&#x60F3;&#x8981;&#x770B;&#x6570;&#x7EC4;&#x4E2D;&#x662F;&#x5426;&#x6709;&#x7B49;&#x4E8E;0&#x7684;&#x6570;&#xFF0C;&#x90A3;&#x4E48;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;&#x4EE5;&#x4E0B;&#x4EE3;&#x7801;&#x6765;&#x5B9E;&#x73B0;&#xFF1A;</p>
<pre><code class="lang-python">np.any(a==<span class="hljs-number">0</span>)
<span class="hljs-comment"># &#x6216;&#x8005;&#x662F;</span>
(a==<span class="hljs-number">0</span>).any()
</code></pre>
<h3 id="&#x6392;&#x5E8F;&#xFF1A;">&#x6392;&#x5E8F;&#xFF1A;</h3>
<ol>
<li><p><code>np.sort</code>&#xFF1A;&#x6307;&#x5B9A;&#x8F74;&#x8FDB;&#x884C;&#x6392;&#x5E8F;&#x3002;&#x9ED8;&#x8BA4;&#x662F;&#x4F7F;&#x7528;&#x6570;&#x7EC4;&#x7684;&#x6700;&#x540E;&#x4E00;&#x4E2A;&#x8F74;&#x8FDB;&#x884C;&#x6392;&#x5E8F;&#x3002;</p>
<pre><code class="lang-python"> a = np.random.randint(<span class="hljs-number">0</span>,<span class="hljs-number">10</span>,size=(<span class="hljs-number">3</span>,<span class="hljs-number">5</span>))
 b = np.sort(a) <span class="hljs-comment">#&#x6309;&#x7167;&#x884C;&#x8FDB;&#x884C;&#x6392;&#x5E8F;&#xFF0C;&#x56E0;&#x4E3A;&#x6700;&#x540E;&#x4E00;&#x4E2A;&#x8F74;&#x662F;1&#xFF0C;&#x90A3;&#x4E48;&#x5C31;&#x662F;&#x5C06;&#x6700;&#x91CC;&#x9762;&#x7684;&#x5143;&#x7D20;&#x8FDB;&#x884C;&#x6392;&#x5E8F;&#x3002;</span>
 c = np.sort(a,axis=<span class="hljs-number">0</span>) <span class="hljs-comment">#&#x6309;&#x7167;&#x5217;&#x8FDB;&#x884C;&#x6392;&#x5E8F;&#xFF0C;&#x56E0;&#x4E3A;&#x6307;&#x5B9A;&#x4E86;axis=0</span>
</code></pre>
<p>&#x8FD8;&#x6709;<code>ndarray.sort()</code>&#xFF0C;&#x8FD9;&#x4E2A;&#x65B9;&#x6CD5;&#x4F1A;&#x76F4;&#x63A5;&#x5F71;&#x54CD;&#x5230;&#x539F;&#x6765;&#x7684;&#x6570;&#x7EC4;&#xFF0C;&#x800C;&#x4E0D;&#x662F;&#x8FD4;&#x56DE;&#x4E00;&#x4E2A;&#x65B0;&#x7684;&#x6392;&#x5E8F;&#x540E;&#x7684;&#x6570;&#x7EC4;&#x3002;</p>
</li>
<li><p><code>np.argsort</code>&#xFF1A;&#x8FD4;&#x56DE;&#x6392;&#x5E8F;&#x540E;&#x7684;&#x4E0B;&#x6807;&#x503C;&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python"> np.argsort(a) <span class="hljs-comment">#&#x9ED8;&#x8BA4;&#x4E5F;&#x662F;&#x4F7F;&#x7528;&#x6700;&#x540E;&#x7684;&#x4E00;&#x4E2A;&#x8F74;&#x6765;&#x8FDB;&#x884C;&#x6392;&#x5E8F;&#x3002;</span>
</code></pre>
</li>
<li><p>&#x964D;&#x5E8F;&#x6392;&#x5E8F;&#xFF1A;<code>np.sort</code>&#x9ED8;&#x8BA4;&#x4F1A;&#x91C7;&#x7528;&#x5347;&#x5E8F;&#x6392;&#x5E8F;&#x3002;&#x5982;&#x679C;&#x6211;&#x4EEC;&#x60F3;&#x91C7;&#x7528;&#x964D;&#x5E8F;&#x6392;&#x5E8F;&#x3002;&#x90A3;&#x4E48;&#x53EF;&#x4EE5;&#x91C7;&#x7528;&#x4EE5;&#x4E0B;&#x65B9;&#x6848;&#x6765;&#x5B9E;&#x73B0;&#xFF1A;</p>
<pre><code class="lang-python"> <span class="hljs-comment"># 1. &#x4F7F;&#x7528;&#x8D1F;&#x53F7;</span>
 -np.sort(-a)

 <span class="hljs-comment"># 2. &#x4F7F;&#x7528;sort&#x548C;argsort&#x4EE5;&#x53CA;take</span>
 indexes = np.argsort(-a) <span class="hljs-comment">#&#x6392;&#x5E8F;&#x540E;&#x7684;&#x7ED3;&#x679C;&#x5C31;&#x662F;&#x964D;&#x5E8F;&#x7684;</span>
 np.take(a,indexes) <span class="hljs-comment">#&#x4ECE;a&#x4E2D;&#x6839;&#x636E;&#x4E0B;&#x6807;&#x63D0;&#x53D6;&#x76F8;&#x5E94;&#x7684;&#x5143;&#x7D20;</span>
</code></pre>
</li>
</ol>
<h3 id="&#x5176;&#x4ED6;&#x51FD;&#x6570;&#x8865;&#x5145;&#xFF1A;">&#x5176;&#x4ED6;&#x51FD;&#x6570;&#x8865;&#x5145;&#xFF1A;</h3>
<ol>
<li><code>np.apply_along_axis</code>&#xFF1A;&#x6CBF;&#x7740;&#x67D0;&#x4E2A;&#x8F74;&#x6267;&#x884C;&#x6307;&#x5B9A;&#x7684;&#x51FD;&#x6570;&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;<pre><code class="lang-python"> <span class="hljs-comment"># &#x6C42;&#x6570;&#x7EC4;a&#x6309;&#x884C;&#x6C42;&#x5747;&#x503C;&#xFF0C;&#x5E76;&#x4E14;&#x8981;&#x53BB;&#x6389;&#x6700;&#x5927;&#x503C;&#x548C;&#x6700;&#x5C0F;&#x503C;&#x3002;</span>
 np.apply_along_axis(<span class="hljs-keyword">lambda</span> x:x[(x != x.max()) &amp; (x != x.min())].mean(),axis=<span class="hljs-number">1</span>,arr=a)
</code></pre>
</li>
<li><code>np.linspace</code>&#xFF1A;&#x7528;&#x6765;&#x5C06;&#x6307;&#x5B9A;&#x533A;&#x95F4;&#x5185;&#x7684;&#x503C;&#x5E73;&#x5747;&#x5206;&#x6210;&#x591A;&#x5C11;&#x4EFD;&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;<pre><code class="lang-python"> <span class="hljs-comment"># &#x5C06;0-1&#x5206;&#x6210;12&#x5206;&#xFF0C;&#x751F;&#x6210;&#x4E00;&#x4E2A;&#x6570;&#x7EC4;</span>
 np.linspace(<span class="hljs-number">0</span>,<span class="hljs-number">1</span>,<span class="hljs-number">12</span>)
</code></pre>
</li>
<li><code>np.unique</code>&#xFF1A;&#x8FD4;&#x56DE;&#x6570;&#x7EC4;&#x4E2D;&#x7684;&#x552F;&#x4E00;&#x503C;&#x3002;<pre><code class="lang-python"> <span class="hljs-comment"># &#x8FD4;&#x56DE;&#x6570;&#x7EC4;a&#x4E2D;&#x7684;&#x552F;&#x4E00;&#x503C;&#xFF0C;&#x5E76;&#x4E14;&#x4F1A;&#x8FD4;&#x56DE;&#x6BCF;&#x4E2A;&#x552F;&#x4E00;&#x503C;&#x51FA;&#x73B0;&#x7684;&#x6B21;&#x6570;&#x3002;</span>
 np.unique(a,return_counts=<span class="hljs-keyword">True</span>)
</code></pre>
</li>
</ol>
<h2 id="&#x66F4;&#x591A;&#xFF1A;">&#x66F4;&#x591A;&#xFF1A;</h2>
<p><a href="https://docs.scipy.org/doc/numpy/reference/index.html" target="_blank">https://docs.scipy.org/doc/numpy/reference/index.html</a></p>

                    
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